Do you mean the three Tasks? First you must press on the word 'Task' (or 'Task 2', etc.) and then you should see an exercise. In the first Task, you must click or press on a word and then click or press on the gap where you want it to go - that should make it move there.

In the other two tasks, you must click or press on the small circle next to the correct answer. At the end, press the Finish button to see if your answers were correct or not. The
Finish
button becomes
Show Answers
- if you press that, the correct answers will appear.

During my business visit, customer was complaining about duplicate items supplied by unauthorized distributor.

I am bringing to your knowledge, pls talk to Mr. Hegde, Mlabs regarding unauthorised distributors supplied by duplicate items. Mlabs has placed the TLC plates order with Orange Trader, Bombay.(unauthorized distributor.) But, they have supplied Merck Duplicate items to Veerasandra plant. Now customer has asked us to give the Merck Authorised Distributors Names. Based on our mail communication, they want to reject the material. So, Can I send a mail communication to Mlabs, like we have authorised only three distributor are chem, Sri scientific SM Scientifics.

5.5 Once again: randomization vs. comparability

Apart from the rather explicit rhetoric of a “valid framework”, there is also always the implicit logic of the experiment. Thus, although the received theory emphasizes that “actual balance has nothing to do with validity of statistical inference; it is an issue of efficiency only” [
41
]; comparability turns out to be crucial:

Many, if not most, of those supporting randomization rush to mention that it promotes similar groups. Nowadays, only a small minority bases its inferences on the known permutation distribution created by the process of randomization; but an overwhelming majority checks for comparability. Reviewers of experimental studies routinely request that authors provide randomization checks, that is, statistical tests designed to substantiate the equivalence of and . At least, in almost every article a list of covariates—with their groupwise means and standard errors—can be found.

A narrow, restricted framework is only able to support weak conclusions; there is no such thing as a “free lunch.” Therefore, upon reaching a strong conclusion, there must be implicit, hidden assumptions (cf. [
19
], pp. 139, 155). In particular, a second look at the “little-assumption” argument reveals that it is the hidden assumption of comparability that carries much of the burden of evidence: It is no coincidence that in Pawitan’s example an eye drug was tested. Suppose one had tested a liver drug instead. The same numerical result would be almost as convincing if such a drug had been applied to twins. However, if the liver drug had been administered to a heterogenous set of persons or if it had been given to a different biological species (mice instead of men, say), exactly the same formal result would not be convincing at all; since, rather obviously, a certain discrepancy a priori may cause a remarkable difference a posteriori.

Savage’s example is quite similar. No matter how one splits a small heterogenous group into two, the latter groups will always be systematically different. Randomization does not help: If you assign randomly and detect a large effect in the end, still, your experimental intervention the initial difference between and may have caused it. All “valid” inferential statistics is, in a sense, an illusion, since it cannot exclude the straightforward second explanation. Instead, it’s the initial exchangeability of the groups that turns out to be decisive; similarity of and rules out the second explanation and leaves the experimental intervention as the only cause.

In conclusion, comparability, much more than randomization, keeps alternative explanations at bay. Since it is our endeavour to achieve similar groups, minimization is not just some supplementary technique to improve efficiency. Rather, it is a straightforward and elaborate device to enhance comparability, i.e., to consciously construct similar groups. (The influence of unknown factors is discussed in Section 4.) Though, at times, we fail, e.g. “it does not seem possible to base a meaningful experiment on a small heterogenous group” [
Tory Burch Miller printed sandals 2iNQISqzSV
], there can hardly be any doubt that “the purpose of randomization is to achieve homogeneity in the sample units. [Thus] it should be spelled out that stability and homogeneity are the foundation of the statistical solution, the other way around” [
HUGO BOSS Mens Highline Cap Toe Oxfords 100 Exclusive gC6Bt
], p. 70 (my emphasis).

In a nutshell, nobody, not even Fisher, follows “pure Frequentist logic”, in particular the distribution that randomization generates. In a strict sense, there is no logic at all, rather a certain kind of mathematical reasoning plus—since the formal framework is restricted to sampling—a fairly large set of conventions; rigid “pure” arguments being readily complemented by applied “flexibility”, consisting of time-honored informal reasoning and shibboleth, but also outright concessions. Bayesians noticed long ago [
28
,
31
] that “Frequentist theory is shot full of contradictions” [
53
], and during the last few decades, efforts to overcome the received framework have gained momentum.

In the 20th century, R.A. Fisher (1890–1962) was the most influential statistician. However, while his early work on mathematical statistics is highly respected in all quarters, hardly anybody relies on his later ideas, in particular fiducial inference [
24
]. Randomization lies in-between, and, quite fittingly, public opinion on this formal technique has remained divided.

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